Computational Characterization of Zr-Oxide MOFs for Adsorption Applications

Zr-oxide secondary building units construct metal–organic framework (MOF) materials with excellent gas adsorption properties and high mechanical, thermal, and chemical stability. These attributes have led Zr-oxide MOFs to be well-recognized for a wide range of applications, including gas storage and separation, catalysis, as well as healthcare domain. Here, we report structure search methods within the Cambridge Structural Database (CSD) to create a curated subset of 102 Zr-oxide MOFs synthesized to date, bringing a unique record for all researchers working in this area. For the identified structures, we manually corrected the proton topology of hydroxyl and water molecules on the Zr-oxide nodes and characterized their textural properties, Brunauer–Emmett–Teller (BET) area, and topology. Importantly, we performed systematic periodic density functional theory (DFT) calculations comparing 25 different combinations of basis sets and functionals to calculate framework partial atomic charges for use in gas adsorption simulations. Through experimental verification of CO2 adsorption in selected Zr-oxide MOFs, we demonstrate the sensitivity of CO2 adsorption predictions at the Henry’s regime to the choice of the DFT method for partial charge calculations. We characterized Zr-MOFs for their CO2 adsorption performance via high-throughput grand canonical Monte Carlo (GCMC) simulations and revealed how the chemistry of the Zr-oxide node could have a significant impact on CO2 uptake predictions. We found that the maximum CO2 uptake is obtained for structures with the heat of adsorption values >25 kJ/mol and the largest cavity diameters of ca. 6–7 Å. Finally, we introduced augmented reality (AR) visualizations as a means to bring adsorption phenomena alive in porous adsorbents and to dynamically explore gas adsorption sites in MOFs.

1 S1. Search criteria in CCDC's structure search software, ConQuest Figure S1. Seven criteria developed for searching for Zr-oxide MOFs in the CSD MOF subset. QA = O, N, P, C, B, S. QB = N, P, B, S, C and superscripts "c" and "a" impose the corresponding atoms to be "cyclic" or "acyclic", respectively. Me denotes methyl groups (redrawn from the work of Moghadam et al. 1 ). Figure S2 shows UiO-66, a well-known Zr-oxide MOF that have been reported in two forms: hydroxylated (Zr6O4(OH)4) and dehydroxylated (Zr6O8). 2 For consistency across all Zr-MOFs, before running the periodic DFT calculations, we manually added the hydroxyl groups for every structure extracted from the CSD MOF subset.

4 Monte
Carlo cycles were performed, the first 50% of cycles were applied for equilibration, and the remaining cycles were applied to calculate the ensemble averages. Insertion, deletion, rotation, and translation moves were set at equal probability. The framework atoms were kept fixed at the crystallographic positions for all Zr-oxide MOFs. Adsorbate-adsorbate and adsorbate-adsorbent interactions were modelled using a Lennard-Jones (LJ) plus Coulomb potential. The force field parameters for nitrogen and carbon dioxide were taken from the TraPPE force field. All force field parameters are tabulated in Tables S1-S3. 1) According to the BET theory 7 , monolayers usually form at pressures of 0.05 < P/P0 < 0.3. We chose this range as the first guess. 2) Plot the left side of eq. (1) versus selected range of relative pressure, perform linear regression to obtain values for C and Nm.
3) Check compliance with consistency criteria 1 and 2. 4) Check whether the selected range from step 1 satisfy criteria 3. If not, pick another range of relative pressure and start from step 2 again. 5) Calculate the value of (1/√C + 1). Check whether the selected range from step 1 satisfies criteria 4. 8 If not, pick another range of relative pressure and start from step 2. 6) Calculate the BET area using eq. 2. = ) . # # . *+ . * # # (2) where: S = surface area, Nm = nitrogen monolayer uptake in m 3 (STP)/g, AN2 = cross section of nitrogen molecule (1.62 x 10 -19 m 2 /molecule), NAV = Avogadro number (6.022 x 10 23 ), * # # = nitrogen molar volume at STP (44.64 mol/m 3 ) Figure S3 shows an example of N2 adsorption isotherm in MOF-812 followed by BET area calculations. We note that we have provided N2 adsorption isotherms and BET area calculations for all 102 Zr-oxide MOFs in the supporting information. 1) Only a range where N(1 -P/Po) increases monotonically with P/Po should be selected.
2) The value of C resulting from the linear regression should be positive.
3) The monolayer loading Nm should correspond to a relative pressure P/Po falling within the selected linear region.    For aqua BOSZEQ, the presence of water molecules prevents CO2 molecules sitting close to the pockets in between ligands and therefore the first RDF peak appears at ca. 5 Å. The proximity of CO2 molecules to the Zr-oxide nodes in BOSZEQ is explained by the dominant MOF-CO2 electrostatic interactions.

S6. MOF-CO2 electrostatic interactions.
DFT simulations were performed with the fully periodic CRYSTAL17 software package. 12 Framework Partial charges were calculated by subtracting the total atomic charge determined by the SCF electronic structure method from the atomic number. For BOSZEQ, EMIYUW, OFAWID, OQUFAJ01-03, QOKBOJ, RUBLAD, UNEJEE, XICYIT and DITJOH structures, we used PBE0 functional and DDEC 13 charge partitioning approach.

Instruction on how to visualize the structure of MOFs using AR
Porous materials such as MOFs has been emerging as new class materials for several applications in chemical industries such as gas adsorption and separation, catalysis, and energy storage material. As of January 2020, it is highly noted that there are 99,075 MOFs available in the Cambridge Structural Database (CSD) MOFs subset. 14 The information about the structure of this materials is essential to be provided and more interestingly if this information could be displayed in three-dimensional (3D) perspectives. However, it may be difficult to convey that information in two-dimensional (2D) space such as paper or computer. Augmented Reality (AR) could help to address this issue where this technology has been being intensively used in computer games and films but underused in chemical science field. This instruction will give you the step by step how to visualize the 3D structure of MOFs in 2D spaces according to the previous work. 15 It is expected that this instruction could become tools in teaching of chemical science and help the students to understand about geometric structures of porous materials, i.e MOFs.
The outline of the instructions explained here has been taken from previous work. 15 Two software (Jmol 16 and Unity 17 ) and the Vuforia platform 18 (Augmented Reality engine) are needed for the MOFs visualization. All of these programs are free to use. Jmol is used to convert molecule structures files (.mol and .cif) into object files (.obj and .mtl) which can be imported to Unity.
Unity is used to setup the application and the Vuforia AR Engine and to assign the molecule objects to specific target images which are setup through Vuforia's online platform. If desired, Unity is also able to build the project into an APK file which can be installed as an app on Android phones. Applications designed by Unity can also be published onto the Play Store and App Store "Open" and "CSD Entry in External Viewer" shown in Fig. S9.  c. Right click and go to the "Style" submenu and ensure Axes, "Boundbox" and "Unit cell" are set to "Hidden" to ensure these elements aren't added into the model when the file is converted into an object. Under the Style submenu, the scheme of the models can also be changed for example from ball and sticks to wireframe shown in Fig. S.11. I find sticks works best for large molecules.  g. Once that command has replied with an "OK" output, the .obj and .mtl files are ready for use. These files are by default saved in the same folder as Jmol (Fig. S.13).

Setting Up Vuforia
• Setting up License a. This is the augmented reality engine that works with unity and has a website which allows you to set up target images for the app.
b. Create an account by registering through this website https://developer.vuforia.com/vui/auth/register. Once registered, log in and go to "License Manager" and "Get a Development Key" (Fig. S.14).   new "Device" type database to store your target images under (Fig. S.17 (a) and b).
Once the database is made, select it from the Target Manager section to begin adding the desired Target Images.   c. Press Import on the Package Import window to add the Vuforia Package to Unity     to "Image Target Behaviour (Script)" (Fig. S.28 (a)).
h. Click on Type and select "From Database" in the dropdown menu ( Fig. S.28 (b)).
Then pick the database and the desired target image from that database you want for this particular layer in the hierarchy. This will place the image target in the middle of the scene, use the middle scene editor window to place this target image to your liking, not overlapping other target images.   • Testing a. If your device has a webcam, you could test the application before moving it to a smartphone. To do this, select AR Camera, go to "Open Vuforia Engine Configuration" in the Inspector window on the right and under "Play mode" select your webcam for the Camera Device ( Fig. S.31(a)).
b. Press play in the main scene window and point your webcam at an example target image to see if it is recognised and an AR MOF is rendered over the target (Fig. S.31 (b)).
c. To finish the testing, press the play button again. c. Navigate to File then "Build Settings" (Fig. S.32 (a)), then choose "Android" and select "Switch Platform" (Fig. S.32 (b)). e. This APK can be shared and used to install the app on other Android devices. Apps made in Unity can also be published in the Play Store. For more detail and clear explanation, we provide video instruction that can be downloaded in this following link. https://www.youtube.com/watch?time_continue=5&v=F_XjZ3Vr6IY